TY -的盟,翼鹏AU -律,Hanjia盟——刘、玉宝盟——张,西洋AU -王,于非盟-罗,Jiebo PY - 2021 DA - 2021/7/18 TI -监控抑郁症在COVID-19 Twitter流行趋势:观察研究乔- JMIR Infodemiology SP - e26769六世- 1 - 1 KW -心理健康KW -抑郁KW -社会媒体KW - Twitter KW -数据挖掘KW -自然语言处理KW -变形金刚KW - COVID-19 AB -背景:新冠肺炎大流行影响了人们的日常生活，在全球范围内造成了经济损失。坊间证据表明，大流行增加了人们的抑郁水平。然而，在大流行期间缺乏抑郁症检测和监测的系统研究。目的:本研究旨在开发一种方法，以自动方式创建大规模抑郁症用户数据集，使该方法具有可扩展性，可以适应未来的事件;验证基于变压器的深度学习语言模型从日常语言中识别抑郁症患者的有效性;心理文本特征在抑郁症分类中的重要性研究最后，利用该模型监测疾病传播过程中不同人群抑郁水平的波动。方法:为了研究这一课题，我们设计了一种有效的基于正则表达式的搜索方法，并创建了最大的英文Twitter抑郁症数据集，其中包含2575个不同的抑郁症识别用户及其过去的推文。为了研究抑郁对人们推特语言的影响，我们在数据集上训练了三个基于变压器的抑郁分类模型，随着训练规模的逐步增加，评估了它们的表现，并比较了模型的推文块级和用户级表现。 Furthermore, inspired by psychological studies, we created a fusion classifier that combines deep learning model scores with psychological text features and users’ demographic information, and investigated these features’ relations to depression signals. Finally, we demonstrated our model’s capability of monitoring both group-level and population-level depression trends by presenting two of its applications during the COVID-19 pandemic. Results: Our fusion model demonstrated an accuracy of 78.9% on a test set containing 446 people, half of which were identified as having depression. Conscientiousness, neuroticism, appearance of first person pronouns, talking about biological processes such as eat and sleep, talking about power, and exhibiting sadness were shown to be important features in depression classification. Further, when used for monitoring the depression trend, our model showed that depressive users, in general, responded to the pandemic later than the control group based on their tweets (n=500). It was also shown that three US states—New York, California, and Florida—shared a similar depression trend as the whole US population (n=9050). When compared to New York and California, people in Florida demonstrated a substantially lower level of depression. Conclusions: This study proposes an efficient method that can be used to analyze the depression level of different groups of people on Twitter. We hope this study can raise awareness among researchers and the public of COVID-19’s impact on people’s mental health. The noninvasive monitoring system can also be readily adapted to other big events besides COVID-19 and can be useful during future outbreaks. SN - 2564-1891 UR - https://infodemiology.www.mybigtv.com/2021/1/e26769 UR - https://doi.org/10.2196/26769 UR - http://www.ncbi.nlm.nih.gov/pubmed/34458682 DO - 10.2196/26769 ID - info:doi/10.2196/26769 ER -